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State-of-the-art sampling-based online POMDP solvers compute near-optimal policies for POMDPs with very large state spaces. However, when faced with large observation spaces, they may become overly optimistic and compute suboptimal policies, because of particle divergence. This paper presents a new online POMDP solver DESPOT-α, which builds upon the widely used DESPOT solver. DESPOT-α improves the practical performance of online planning for POMDPs with large observation as well as state spaces. Like DESPOT, DESPOTα uses the particle belief approximation and searches a determinized sparse belief tree. To tackle large observation spaces, DESPOT-α shares sub-policies among many observations during online policy computation. The value function of a sub-policy is a linear function of the belief, commonly known as α-vector. We introduce a particle approximation of the α-vector to improve the efficiency of online policy search. We further speed up DESPOTα using CPU and GPU parallelization ideas introduced in HyP-DESPOT. Experimental results show that DESPOT-α/HyP-DESPOT-α outperform DESPOT/HyP-DESPOT on POMDPs with large observation spaces, including a complex simulation task involving an autonomous vehicle driving among many pedestrians.

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Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.

Timetabling problem is known as an NP-hard problem that centres around finding an optimized allocation of subjects onto a finite available number of slots and spaces. It is perhaps the most challenging issues looked by colleges around the globe. Every academic institution faces a problem when preparing courses and exam plans. There are many restrictions raised while preparing a timetable. This paper proposed a method based on the evolutionary algorithms to solve the constrained timetable problem, which helps to create theory as well as lab schedule for universities. A smart adaptive mutation scheme is used to speed up convergence and chromosome format is also problem specific. Here in this paper two algorithms are compared in respect of Timetabling problems. Using GA (Genetic Algorithm) and MA (Memetic algorithm), we optimised the output by selecting the best solution from the available options to present a comprehensive curriculum system.

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